documentation

susiepca/infer.py

@@ -25,8 +25,24 @@ def logdet(A):
     return ldet
 
 
-# Define the class for model parameters
 class ModelParams(NamedTuple):
+    """Define the class for variational parameters. This
+        class include variational parameters of all the variable
+        we need to infer from the SuSiE PCA
+
+    Args:
+        mu_z: mean parameter for factor Z
+        var_z: variance parameter for factor Z
+        mu_w: conditional mean parameter for loadings W
+        var_w: conditional variance parameter for loading W
+        alpha: parameter for the gamma that follows multinomial
+                distribution
+        tau: inverse variance parameter of observed data X
+        tau_0: inverse variance parameter of single effect w_kl
+        pi: prior probability for gamma
+
+    """
+
     # variational params for Z
     mu_z: jnp.ndarray
     var_z: jnp.ndarray
@@ -46,10 +62,11 @@ class ModelParams(NamedTuple):
     pi: jnp.ndarray
 
 
-# Define the class of all components in ELBO.
+#
 class ELBOResults(NamedTuple):
 
-    """
+    """Define the class of all components in ELBO,
+        which is returned by function ``compute_elbo``
 
     Args:
         elbo: the value of ELBO
@@ -75,6 +92,17 @@ def __str__(self):
 
 
 class SuSiEPCAResults(NamedTuple):
+    """Define the object returned by function ``susie_pca``
+
+    Args:
+        params: the dictionary contain all the infered parameters
+        elbo: the value of ELBO
+        pve: the ndarray of percent of variance explained
+        pip: the ndarray of posterior inclusion probabilities
+        W: the posterior mean parameter for loadings
+
+    """
+
     params: ModelParams
     elbo: ELBOResults
     pve: jnp.ndarray
@@ -301,13 +329,13 @@ def compute_elbo(X, params) -> ELBOResults:
     return result
 
 
-# Create a function to compute the posterior inclusion probabilities (PIPs).
 def compute_pip(params):
 
-    """
+    """Create a function to compute the posterior inclusion probabilities (PIPs).
 
     Args:
-        params: the dictionary return from the function ``susie_pca``.
+        params: the dictionary contains all the infered parameters,
+                returned from the function ``susie_pca``.
 
     Returns:
         pip: the K by P array of posterior inclusion probabilities (PIPs)
@@ -323,7 +351,8 @@ def compute_pve(params):
     """Create a function to compute the percent of variance explained (PVE).
 
     Args:
-        params: the dictionary return from the function susie_pca
+        params: the dictionary contains all the infered parameters,
+        returned from the function ``susie_pca``
 
     Returns:
         pve: the length K array of percent of variance explained by each factor (PVE)

susiepca/metrics.py

@@ -7,8 +7,8 @@
 
 
 def mse(X: jnp.ndarray, Xhat: jnp.ndarray):
-    """
-
+    """Create a function to compute relative
+        roote mean square error.
 
     Args:
         X : Input data. Should be a array-like
@@ -23,7 +23,8 @@ def mse(X: jnp.ndarray, Xhat: jnp.ndarray):
 
 def get_credset(params, rho=0.9):
 
-    """
+    """Creat a function to compute the rho-level
+        credible set
 
     Args:
         params: the dictionary return from the function susie_pca